Machine Learning Approach To Design OfComplex Distillation Columns

Abstract

Automation of the knowledge acquisition process in building knowledgebased systems for process design is addressed through Machine Learning techniques. A hybrid Machine Learning algorithm developed at the University of South Florida is presented as a knowledge acquisition tool for developing knowledge-based systems. The learning algorithm addresses the knowledge acquisition problem by developing and maintaining the knowledge base through inductive learning from the examples. The learning algorithm named as Symbolic-Connectionist net (SCnet), overcomes the problems associated with neural and symbolic learning systems by integrating the symbolic information into a neural network representation. The learning system allows for knowledge extraction and background knowledge encoding in the form of rules. Fuzzy logic has been made use of in dealing with uncertainty in the learning domain. The description language for the learning system consists of continuous and discrete variables along with relational and fuzzy comparators. The applicability of the learning system for process design is illustrated through a complex column sequencing example. The performance of the learning system is discussed in terms of the knowledge extracted from example cases and its classification accuracy on the test cases. Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517